API design for machine learning software: experiences from the scikit-learn project

Scikit-learn is an increasingly popular machine learning li- brary. Written in Python, it is designed to be simple and efficient, accessible to non-experts, and reusable in various contexts. In this paper, we present and discuss our design choices for the application programming interface (API) of the project. In particular, we describe the simple and elegant interface shared by all learning and processing units in the library and then discuss its advantages in terms of composition and reusability. The paper also comments on implementation details specific to the Python ecosystem and analyzes obstacles faced by users and developers of the library

Enteral Vancomycin to Eliminate MRSA Carriership of the Digestive Tract in Critically Ill Patients

Background: Carriership with methicillin resistant Staphylococcus aureus (MRSA) is a risk for the development of secondary infections in critically ill patients. Previous studies suggest that enteral vancomycin is able to eliminate enteral carriership with MRSA. Data on individual effects of this treatment are lacking. Methods: Retrospective analysis of a database containing 15 year data of consecutive patients from a mixed medical-(cardio)surgical 18 bedded intensive care unit was conducted. All consecutive critically ill patients with enteral MRSA carriership detected in throat and/or rectal samples were collected. We analyzed those with follow-up cultures to determine the success rate of enteral vancomycin. Topical application of 2% vancomycin in a sticky oral paste was performed combined with a vancomycin solution of 500 mg four times daily in the nasogastric tube. This treatment was added to a regimen of selective digestive tract decontamination (SDD) to prevent ICU acquired infection. Results: Thirteen patients were included. The mean age was 65 years and the median APACHE II score was 21. MRSA was present in the throat in 8 patients and in both throat and rectum in 5 patients. In all patients MRSA was successfully eliminated from both throat and rectum, which took 2–11 days with a median duration until decontamination of 4 days. Secondary infections with MRSA did not occur. Conclusions: Topical treatment with vancomycin in a 2% sticky oral paste four times daily in the nasogastric tube was effective in all patients in the elimination of MRSA and prevented secondary MRSA infections

Eradication of Resistant and Susceptible Aerobic Gram-Negative Bacteria From the Digestive Tract in Critically Ill Patients; an Observational Cohort Study

BACKGROUND: Selective Decontamination of the Digestive tract (SDD) aims to prevent nosocomial infections, by eradication of potentially pathogenic micro-organisms from the digestive tract. OBJECTIVES: To estimate the rate of and the time to eradication of resistant vs. susceptible facultative aerobic gram-negative bacteria (AGNB) in patients treated with SDD. METHODS: This observational and retrospective study included patients admitted to the ICU between January 2001 and August 2017. Patients were included when treated with SDD (tobramycin, polymyxin B, and amphotericin B) and colonized in the upper or lower gastro-intestinal (GI) tract with at least one AGNB present on admission. Decontamination was determined after the first negative set of cultures (rectal and throat). An additional analysis was performed of two consecutive negative cultures. RESULTS: Of the 281 susceptible AGNB in the throat and 1,087 in the rectum on admission, 97.9 and 93.7%, respectively, of these microorganisms were successfully eradicated. In the upper GI-tract no differences in eradication rates were found between susceptible and resistant microorganisms. However, the median duration until eradication was significantly longer for aminoglycosides resistant vs. susceptible microorganisms (5 vs. 4 days, p < 0.01). In the lower GI-tract, differences in eradication rates between susceptible and resistant microorganisms were found for cephalosporins (90.0 vs. 95.6%), aminoglycosides (84.4 vs. 95.5%) and ciprofloxacin (90.0 vs. 95.2%). Differences in median duration until eradication between susceptible and resistant microorganisms were found for aminoglycosides and ciprofloxacin (both 5 days vs. 6 days, p = 0.001). Decontamination defined as two negative cultures was achieved in a lower rate (77–98% for the upper GI tract and 64–77% for the lower GI tract) and a median of 1 day later. CONCLUSION: The vast majority of both susceptible and resistant microorganisms are effectively eradicated from the upper and lower GI tract. In the lower GI tract decontamination rates of susceptible microorganisms are significantly higher and achieved in a shorter time period compared to resistant strains

Designing and evaluating the usability of a machine learning API for rapid prototyping music technology

To better support creative software developers and music technologists' needs, and to empower them as machine learning users and innovators, the usability of and developer experience with machine learning tools must be considered and better understood. We review background research on the design and evaluation of application programming interfaces (APIs), with a focus on the domain of machine learning for music technology software development. We present the design rationale for the RAPID-MIX API, an easy-to-use API for rapid prototyping with interactive machine learning, and a usability evaluation study with software developers of music technology. A cognitive dimensions questionnaire was designed and delivered to a group of 12 participants who used the RAPID-MIX API in their software projects, including people who developed systems for personal use and professionals developing software products for music and creative technology companies. The results from the questionnaire indicate that participants found the RAPID-MIX API a machine learning API which is easy to learn and use, fun, and good for rapid prototyping with interactive machine learning. Based on these findings, we present an analysis and characterization of the RAPID-MIX API based on the cognitive dimensions framework, and discuss its design trade-offs and usability issues. We use these insights and our design experience to provide design recommendations for ML APIs for rapid prototyping of music technology. We conclude with a summary of the main insights, a discussion of the merits and challenges of the application of the CDs framework to the evaluation of machine learning APIs, and directions to future work which our research deems valuable

Adversarial Attacks on Classifiers for Eye-based User Modelling

An ever-growing body of work has demonstrated the rich information content available in eye movements for user modelling, e.g. for predicting users' activities, cognitive processes, or even personality traits. We show that state-of-the-art classifiers for eye-based user modelling are highly vulnerable to adversarial examples: small artificial perturbations in gaze input that can dramatically change a classifier's predictions. We generate these adversarial examples using the Fast Gradient Sign Method (FGSM) that linearises the gradient to find suitable perturbations. On the sample task of eye-based document type recognition we study the success of different adversarial attack scenarios: with and without knowledge about classifier gradients (white-box vs. black-box) as well as with and without targeting the attack to a specific class, In addition, we demonstrate the feasibility of defending against adversarial attacks by adding adversarial examples to a classifier's training data.Comment: 9 pages, 7 figure

Clust-IT:Clustering-Based Intrusion Detection in IoT Environments

Low-powered and resource-constrained devices are forming a greater part of our smart networks. For this reason, they have recently been the target of various cyber-attacks. However, these devices often cannot implement traditional intrusion detection systems (IDS), or they can not produce or store the audit trails needed for inspection. Therefore, it is often necessary to adapt existing IDS systems and malware detection approaches to cope with these constraints. We explore the application of unsupervised learning techniques, specifically clustering, to develop a novel IDS for networks composed of low-powered devices. We describe our solution, called Clust-IT (Clustering of IoT), to manage heterogeneous data collected from cooperative and distributed networks of connected devices and searching these data for indicators of compromise while remaining protocol agnostic. We outline a novel application of OPTICS to various available IoT datasets, composed of both packet and flow captures, to demonstrate the capabilities of the proposed techniques and evaluate their feasibility in developing an IoT IDS

Atrial Fibrosis Hampers Non-invasive Localization of Atrial Ectopic Foci From Multi-Electrode Signals: A 3D Simulation Study

[EN] Introduction: Focal atrial tachycardia is commonly treated by radio frequency ablation with an acceptable long-term success. Although the location of ectopic foci tends to appear in specific hot-spots, they can be located virtually in any atrial region. Multi-electrode surface ECG systems allow acquiring dense body surface potential maps (BSPM) for non-invasive therapy planning of cardiac arrhythmia. However, the activation of the atria could be affected by fibrosis and therefore biomarkers based on BSPM need to take these effects into account. We aim to analyze the effect of fibrosis on a BSPM derived index, and its potential application to predict the location of ectopic foci in the atria. Methodology: We have developed a 3D atrial model that includes 5 distributions of patchy fibrosis in the left atrium at 5 different stages. Each stage corresponds to a different amount of fibrosis that ranges from 2 to 40%. The 25 resulting 3D models were used for simulation of Focal Atrial Tachycardia (FAT), triggered from 19 different locations described in clinical studies. BSPM were obtained for all simulations, and the body surface potential integral maps (BSPiM) were calculated to describe atrial activations. A machine learning (ML) pipeline using a supervised learning model and support vector machine was developed to learn the BSPM patterns of each of the 475 activation sequences and relate them to the origin of the FAT source. Results: Activation maps for stages with more than 15% of fibrosis were greatly affected, producing conduction blocks and delays in propagation. BSPiMs did not always cluster into non-overlapped groups since BSPiMs were highly altered by the conduction blocks. From stage 3 (15% fibrosis) the BSPiMs showed differences for ectopic beats placed around the area of the pulmonary veins. Classification results were mostly above 84% for all the configurations studied when a large enough number of electrodes were used to map the torso. However, the presence of fibrosis increases the area of the ectopic focus location and therefore decreases the utility for the electrophysiologist. Conclusions: The results indicate that the proposed ML pipeline is a promising methodology for non-invasive ectopic foci localization from BSPM signal even when fibrosis is present.This work was partially supported by Ministerio de Economia y Competitividad and Fondo Europeo de Desarrollo Regional (FEDER) DPI2015-69125-R and TIN2014-59932-JIN (MINECO/FEDER, UE).Godoy, EJ.; Lozano, M.; García-Fernández, I.; Ferrer-Albero, A.; Macleod, R.; Saiz, J.; Sebastián, R. (2018). Atrial Fibrosis Hampers Non-invasive Localization of Atrial Ectopic Foci From Multi-Electrode Signals: A 3D Simulation Study. Frontiers in Physiology. 9:1-18. https://doi.org/10.3389/fphys.2018.00404S1189Boyle, P. 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Clustering Algorithms: Their Application to Gene Expression Data

Gene expression data hide vital information required to understand the biological process that takes place in a particular organism in relation to its environment. Deciphering the hidden patterns in gene expression data proffers a prodigious preference to strengthen the understanding of functional genomics. The complexity of biological networks and the volume of genes present increase the challenges of comprehending and interpretation of the resulting mass of data, which consists of millions of measurements; these data also inhibit vagueness, imprecision, and noise. Therefore, the use of clustering techniques is a first step toward addressing these challenges, which is essential in the data mining process to reveal natural structures and iden-tify interesting patterns in the underlying data. The clustering of gene expression data has been proven to be useful in making known the natural structure inherent in gene expression data, understanding gene functions, cellular processes, and subtypes of cells, mining useful information from noisy data, and understanding gene regulation. The other benefit of clustering gene expression data is the identification of homology, which is very important in vaccine design. This review examines the various clustering algorithms applicable to the gene expression data in order to discover and provide useful knowledge of the appropriate clustering technique that will guarantee stability and high degree of accuracy in its analysis procedure